Audio samples can be recorded by many commercially available electronic devices such as smart phones, tablets, e-readers, computers, personal digital assistants, personal media players, etc. Audio matching provides for the identification of a recorded audio sample by comparing the audio sample to a set of reference samples. To make the comparison, an audio sample can be transformed to a time-frequency representation of the sample by using, for example, a short time Fourier transform (STFT). Using the time-frequency representation, interest points that characterize time and/or frequency locations of peaks or other distinct patterns of the spectrogram can then be extracted from the audio sample. Fingerprints or descriptors can then be computed as functions of sets of interest points. Fingerprints of the audio sample can then be compared to fingerprints of reference samples to determine identity of the audio sample.
It is desirable that an audio matching system contain interest points that are robust to the presence of noise, pitch shifting, time stretching, compression techniques, or other types of distortion in the audio sample that can prevent accurate identification of the audio sample. However, as fingerprints are computed as functions of sets of interest points, the more interest points present in a fingerprint, the less scalable the audio matching system becomes.